Abstract
Individuals together with their locations & attributes are essential to feed micro-level applied urban models (for example, spatial micro-simulation and agent-based modeling) for policy evaluation. Existing studies on population spatialization and population synthesis are generally separated. In developing countries like China, population distribution on a fine scale, as the input for population synthesis, is not universally available. With the open-government initiatives in China and the emerging Web 2.0 techniques, more and more open data are becoming achievable. In this chapter, we propose an automatic process using open data for population spatialization and synthesis. Specifically, the road network in OpenStreetMap is used to identify and delineate parcel geometries, while crowd-sourced points of interest (POIs) are gathered to infer urban parcels with a vector cellular automata model. Housing-related online check-in records are then applied to distinguish residential parcels from all of the identified urban parcels. Finally the published census data, in which the sub-district level of attributes distribution and relationships among attributes are available, is used for synthesizing population attributes with a previously developed tool Agenter (Long and Shen 2013). The results are validated with ground truth manually-prepared dataset by planners from Beijing Institute of City Planning.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The spatial distribution of census tracts has never been released by the Beijing Municipal Statistical Bureau. Therefore, it is not possible to determine whether census tracts are compatible with TAZs.
- 2.
Residential POIs within the buffered road space were accounted by their closest parcels in our experiment.
- 3.
The unit is the POI count per km2. For parcels with no residential POIs, we assume a minimum density of 1 POI per km2.
- 4.
Parcels by ORDNANCE in Beijing were similar with those by planners in BICP in terms of parcel size.
References
Azar, D., Ryan, E., Jordan, G., et al. (2013). Generation of fine-scale population layers using multi-resolution satellite imagery and geospatial data. Remote Sensing of Environment, 130, 219–232.
Barthelemy, J., & Toint, P. L. (2013). Synthetic population generation without a sample. Transportation Science, 47(2), 266–279.
Beijing Municipal Bureau of Statistics, NBS Survey Office in Beijing. (2013). Beijing statistical yearbook 2013. Beijing: China Statistic Press.
Bhaduri, B., Bright, E., Coleman, P., & Urban, M. L. (2007). LandScan USA: A high-resolution geospatial and temporal modeling approach for population distribution and dynamics. GeoJournal, 69(1–2), 103–117.
Haklay, M., & Weber, P. (2008). OpenStreetMap: User-generated street maps. IEEE Pervasive Computing, 7(4), 12–18.
Hermes, K., & Poulsen, M. (2012). A review of current methods to generate synthetic spatial microdata using reweighting and future directions. Computers, Environment and Urban Systems, 36(4), 281–290.
Langford, M., & Unwin, D. J. (1994). Generating and mapping population density surfaces within a geographical information system. The Cartographic Journal, 31, 21–26.
Liao, Y., Wang, J., Meng, B., & Li, X. (2010). Integration of GP and GA for mapping population distribution. International Journal of Geographical Information Science, 24, 47–67.
Linard, C., Gilbert, M., Snow, R. W., et al. (2012). Population distribution, settlement patterns and accessibility across Africa in 2010. PloS One, 7(2), e31743.
Liu, Y., Sui, Z., Kang, C., & Gao, Y. (2014). Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data. PloS One, 9(1), e86026. doi:10.1371/journal.pone.0086026.
Lo, C. P. (2001). Modeling the population of China using DMSP operational linescan system nighttime data. Photogrammetric Engineering & Remote Sensing, 67(9), 1037–1047.
Long, Y., & Liu, X. (2013). Automated identification and characterization of parcels (AICP) with OpenStreetMap and points of interest. arXiv preprint.arXiv:1311.6165.
Long, Y., & Shen, Z. (2013). Disaggregating heterogeneous agent attributes and location from aggregated data, small-scale surveys and empirical researches. Computers, Environment and Urban Systems, 42, 14–25.
Long, Y., & Shen, Y. (2014). Mapping parcel-level urban areas for a large geographical area. arXiv preprint.arXiv:1403.5864.
Mennis, J. (2003). Generating surface models of population using dasymetric mapping. The Professional Geographer, 55, 31–42.
Müller, K., & Axhausen, K. W. (2010). Population synthesis for microsimulation: State of the art. The 10th Swiss transport research conference. Zurich: ETH.
Silva, F. B., Gallego, J., & Lavalle, C. (2013). A high-resolution population grid map for Europe. Journal of Maps, 9(1), 1–13.
Silvan, J., Wang, L., Rogerson, P., et al. (2010). Assessing fine spatial resolution remote sensing for small area population estimation. International Journal of Remote Sensing, 31(21), 5605–5634.
Sun, L., Axhausen, K. W., Lee, D. H., & Huang, X. (2013). Understanding metropolitan patterns of daily encounters. PNAS, 110(34), 13774–13779.
Sutton, P. (1997). Modeling population density with night-time satellite imagery and GIS. Computers, Environment and Urban Systems, 21(3), 227–244.
Tatem, A. J., & Linard, C. (2011). Population mapping of poor countries. Nature, 474, 36.
Wu, B. M., Birkin, M. H., & Rees, P. H. (2008). A spatial microsimulation model with student agents. Computers, Environment and Urban Systems, 32, 440–453.
Acknowledgements
We thank Ms Liqun Chen for her proofreading.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Long, Y., Shen, Z. (2015). Population Spatialization and Synthesis with Open Data. In: Geospatial Analysis to Support Urban Planning in Beijing. GeoJournal Library, vol 116. Springer, Cham. https://doi.org/10.1007/978-3-319-19342-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-19342-7_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19341-0
Online ISBN: 978-3-319-19342-7
eBook Packages: Humanities, Social Sciences and LawSocial Sciences (R0)